Mobile robots can rely on active sensors to detect their environment. Such sensors can be used to identify absolute or relative position, create maps through simultaneous localization and mapping (SLAM), and detect obstacles such as walls or movable objects. Obstacle detection is particularly important for avoiding unwanted collisions with moving vehicles, humans, animals, or even other robots. Such obstacle avoidance commonly relies on long-distance rangefinders that actively scan the environment using laser, infrared, or sonar beams. While such active range finding sensor systems can provide highly accurate centimeter scale position data on millisecond timescales, they can be relatively expensive. For example, laser-based sensors with a wide field of view (up to 270°) can sense stationary or moving obstacles up to 30 meters away by projecting a long-range laser beam and scanning it to bounce off any obstacles in order to detect the distance to the closest obstacle along that beam's path. This effectively delivers a view of obstacles in a 270° field of view around the sensor, and provides mapping and/or obstacle avoidance data that can be used by robot operating system software (ROS) such as provided by the Open Source Robotics Foundation.
Unfortunately, such conventional sensor arrangements can be costly and too delicate for many types of applications. Since sensor systems are a significant component in a robot bill of materials, providing low cost commercial robots depends at least in part upon use of low cost robot sensor systems that are effective, rugged, and simple to calibrate and assemble.